CN111509782A - Probabilistic power flow analysis method considering charging load and photovoltaic output random characteristics - Google Patents

Probabilistic power flow analysis method considering charging load and photovoltaic output random characteristics Download PDF

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CN111509782A
CN111509782A CN202010300189.1A CN202010300189A CN111509782A CN 111509782 A CN111509782 A CN 111509782A CN 202010300189 A CN202010300189 A CN 202010300189A CN 111509782 A CN111509782 A CN 111509782A
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power
distributed photovoltaic
probability
clustering
electric vehicle
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CN111509782B (en
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钱科军
李亚飞
马千里
周磊
刘乙
冯亦凡
张新松
卢成
徐杨杨
陆胜男
曹书秀
朱建峰
姜柯柯
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Nantong University
Suzhou Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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Suzhou Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy

Abstract

The invention relates to the field of power distribution system load flow calculation, in particular to a probabilistic load flow analysis method considering random characteristics of charging load and photovoltaic output. Distributed photovoltaic power stations and electric vehicle charging stations are random disturbance sources in a power distribution system, and the random characteristics of power generation output and charging load determine the random characteristics of power flow distribution of the whole power distribution system. The method and the device can be used for performing rapid probability power flow analysis on the power distribution system after the distributed photovoltaic and electric vehicle charging station is simultaneously accessed, and provide decision reference for planners.

Description

Probabilistic power flow analysis method considering charging load and photovoltaic output random characteristics
Technical Field
The invention relates to the field of power distribution system load flow calculation, in particular to a probabilistic load flow analysis method considering random characteristics of charging load and photovoltaic output.
Background
In recent years, with the gradual depletion of petroleum resources and the increasing increase of environmental pollution, clean energy vehicles represented by electric vehicles are becoming popular. An electric vehicle charging station is one of the main places for charging electric vehicles, and when a large number of electric vehicles are charged simultaneously, the charging power of a single charging station can reach hundreds of kilowatts and even thousands of kilowatts, and the charging station is an important load in a power distribution system. Under the influence of uncertain factors such as traffic behaviors of car owners, charging habits and the like, the charging load of the electric car charging station has a remarkable random characteristic. Meanwhile, the permeability of the distributed photovoltaic power station in a power distribution system is increased day by day, and the output of the distributed photovoltaic power station also has an obvious random characteristic due to the primary energy. Under the combined action of the two random factors, the operation condition of the power distribution system inevitably presents strong random characteristics, the traditional deterministic trend is difficult to accurately describe the operation condition of the power distribution system, and the probability evaluation must be carried out on the operation condition of the power distribution system by means of the probabilistic trend. In the planning problem related to the power distribution system, a planner needs to repeatedly perform planning scheme selection based on a probability power flow result, so that a higher requirement is put on the probability power flow analysis speed of the power distribution system. In summary, it is urgently needed to provide a power distribution system probabilistic power flow analysis method considering random characteristics of charging load and photovoltaic output, so as to perform rapid probabilistic power flow analysis on a power distribution system after a distributed photovoltaic and electric vehicle charging station is simultaneously accessed, and provide a decision reference for planning staff.
In the literature, "probabilistic power flow calculation based on improved Latin hypercube sampling by evolutionary algorithm" (the Chinese Motor engineering journal, 2011, volume 31, phase 25, pages 90 to 96), the power system probabilistic power flow calculation is performed by adopting a Monte Carlo simulation method based on Latin hypercube sampling, and the probabilistic power flow analysis efficiency is improved by an improved median Latin hypercube sampling method containing the evolutionary algorithm. The method provided by the document can provide an accurate probability load flow analysis result, but an accurate random variable probability distribution model is required, the calculation amount is large, the calculation speed is slow, and the rapid probability load flow analysis of the power distribution system after the distributed photovoltaic and electric vehicle charging station is simultaneously accessed is difficult. In document two, "evaluation of voltage state of a distribution network containing distributed photovoltaic based on a probabilistic power flow method" (protection and control of a power system, 2019, volume 47, phase 2, pages 123 to 130) a power distribution system probabilistic power flow analysis method based on semi-invariant combined Gram-Charlier series expansion is proposed, and probabilistic power flow calculation is performed on a power distribution system after distributed photovoltaic access. In the third document, "planning strategy of electric vehicle charging station based on distribution network probability power flow calculation" (protection and control of power system, 2019, volume 47, period 22, pages 9 to 16), a power distribution system after large-scale electric vehicle charging is subjected to probability power flow analysis by using a semi-invariant and Gram-Charlier series. Like the second document, the third document proposes a method with high efficiency, but the power distribution system power flow model is required to be subjected to linear approximation, and the calculation error is large.
The charging load of the electric vehicle charging station and the output of the distributed photovoltaic power station have random characteristics, and the operation condition of the power distribution system inevitably has extremely strong random characteristics under the combined action of the two random factors. Therefore, in the planning problem related to the power distribution system, the planner needs to repeatedly perform planning scheme selection based on the probability power flow result. That is to say, it is necessary to provide a power distribution system probabilistic power flow analysis method considering charging load and photovoltaic output, so as to perform fast probabilistic power flow analysis on a power distribution system to which the distributed photovoltaic and electric vehicle charging stations are simultaneously connected, and provide a decision reference for planning staff. However, the prior art method is low in efficiency or large in calculation error, and cannot meet the requirements of planners.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a probabilistic power flow analysis method considering the random characteristics of charging load and photovoltaic output. And (4) carrying out rapid probability power flow analysis on the power distribution system after the distributed photovoltaic and electric vehicle charging station is simultaneously accessed, and providing decision reference for power distribution system planners.
In order to achieve the purpose, the invention adopts the following technical scheme:
the probability power flow analysis method considering the random characteristics of the charging load and the photovoltaic output comprises the following steps:
s1: setting parameters required by the original data and the probability power flow analysis, wherein the parameters comprise: the method comprises the following steps of (1) a power distribution system topological structure, power distribution branch impedance parameters, active and reactive load curves of a power distribution node in a typical day, access positions of a distributed photovoltaic power station and an electric vehicle charging station, a historical sunrise output curve set of the distributed photovoltaic power station, a historical sunrise charging load curve set of the electric vehicle charging station, clustering numbers for clustering the historical sunrise output curve set of the distributed photovoltaic power station and the historical sunrise charging load curve set of the electric vehicle charging station, and the number of tide analysis periods in the typical day;
s2: clustering a historical sunrise output curve set of the distributed photovoltaic power station by adopting a K-means clustering method, and constructing a distributed photovoltaic output probability scene set capable of reflecting the random characteristic of the distributed photovoltaic output;
s3: clustering a historical daily charging load curve set of the electric vehicle charging station by adopting a K-means clustering method, and constructing an electric vehicle charging station charging load probability scene set capable of embodying the random characteristic of the charging load of the electric vehicle charging station;
s4: constructing a power distribution system power flow analysis probability scene set in a typical day on the basis of the distributed photovoltaic output probability scene set and the charging load probability scene set of the electric vehicle charging station;
s5: and carrying out power distribution system power flow calculation under all scenes in the power distribution system power flow analysis probability scene set by adopting an approximate forward-backward substitution method, summarizing power flow calculation results according to scene probabilities in the power flow analysis probability scene set, and giving power distribution system probability power flow analysis results in a typical day.
As a preferred technical scheme of the invention: the step of constructing the distributed photovoltaic output probability scene set in step S2 is specifically as follows: s2.1: the number of clusters is nc-pvFrom n to nt-pvRandomly selecting n from historical solar output curves of distributed photovoltaic panelsc-pvTaking the strip curve as an initial clustering center; s2.2: calculating the distance from all the distributed photovoltaic sunrise force curves to each clustering center according to a formula (1), and classifying the distances into clusters represented by the clustering centers closest to each clustering center;
Figure BDA0002453697330000031
in the formula (1), the first and second groups,pv,iconcentrating the ith photovoltaic output curve (i ═ 1,2, ·, n) for the distributed photovoltaic historical output curvet-pv)；
Figure BDA0002453697330000032
The jth clustering center (j ═ 1,2, ·, n) of the distributed photovoltaic output curve setc-pv)；d(pv,i,
Figure BDA0002453697330000033
) Is the ith photovoltaic output curve and the jthDistance between cluster centers; t is a power flow analysis time interval index (T is 1,2, T); t is the number of power flow analysis time periods in a typical day;pv,i,tgenerating power of the ith photovoltaic output curve in a power flow analysis time period t;
Figure BDA0002453697330000034
generating power of the jth clustering center in a power flow analysis time period t;
s2.3: calculating the mean value of all distributed photovoltaic output curves in each cluster according to a formula (2), taking the mean value as a new cluster center of each cluster, then calculating the distance from all distributed photovoltaic output curves in each cluster to the cluster center according to a formula (1), and calculating the distance mean value according to a formula (3);
Figure BDA0002453697330000035
Figure BDA0002453697330000036
in the formula (2) and the formula (3), npv,jThe number of distributed photovoltaic output curves in the jth cluster; omegajIs a set of distributed photovoltaic output curve indexes in the jth cluster; dpv-av,jThe average value of the distances from all the distributed photovoltaic output curves to the clustering center in the jth cluster;
s2.4: judging each clustering center
Figure BDA0002453697330000037
And whether the average value of the distances from all distributed photovoltaic output curves to the clustering center in the clustering is changed or not; if the change occurs, skipping to the step S2.2, and continuing clustering; otherwise, outputting a clustering result, and constructing a distributed photovoltaic output probability scene set and a clustering center
Figure BDA0002453697330000038
Namely a distributed photovoltaic output scene with concentrated probability scenes, and the probability p corresponding to each scenepv,jCan be calculated by formula (4)Calculating:
Figure BDA0002453697330000039
in the formula (4), npv,jIs the number of distributed photovoltaic output curves in the jth cluster.
As a preferred technical scheme of the invention: the step of constructing the charging load probability scene set of the electric vehicle charging station in the step S3 is specifically as follows: s3.1: the number of clusters is nc-evFrom n to nt-evRandomly selecting n from historical daily charging load curve of electric vehicle charging stationc-evTaking the strip curve as an initial clustering center; s3.2: calculating the distance from the charging load curve to each clustering center on all days according to a formula (5), and classifying the distance into the cluster represented by the clustering center closest to the charging load curve;
Figure BDA00024536973300000310
in the formula (5), the first and second groups,ev,ian ith charging load curve (i ═ 1,2, ·, n) in the charging station historical daily charging load curve sett-ev)；
Figure BDA0002453697330000041
Charging the jth cluster center of the load curve set for the electric vehicle charging station (j ═ 1,2, ·, n ·c-ev)；d(ev,i,
Figure BDA0002453697330000042
) The distance between the ith charging load curve and the jth clustering center is obtained;ev,i,tcharging power of the ith charging load curve in a power flow analysis time period t;
Figure BDA0002453697330000043
charging power of the jth clustering center in a power flow analysis time period t;
s3.3: calculating the mean value of all charging load curves in each cluster according to a formula (6), making the mean value as a new cluster center of each cluster, then calculating the distance from all charging load curves in each cluster to the cluster center according to a formula (5), and calculating the distance mean value according to a formula (7);
Figure BDA0002453697330000044
Figure BDA0002453697330000045
in the formula (6) and the formula (7), nev,jIs the number of charging load curves in the jth cluster;jis the set of charging load curve indices in the jth cluster; dev-av,jThe average value of the distances from all the charging load curves to the cluster center in the jth cluster; s3.4: judging each clustering center
Figure BDA0002453697330000046
And whether the average value of the distances from all the charging load curves to the clustering center in the clustering is changed or not; if the change occurs, skipping to the step S3.2, and continuing clustering; otherwise, outputting a clustering result, constructing a charging load probability scene set and a clustering center of the electric vehicle charging station
Figure BDA0002453697330000047
That is, the charging load scenario with a concentrated probability scenario, and the probability p corresponding to each scenarioev,jCan be calculated from equation (8):
Figure BDA0002453697330000048
in the formula (8), nev,jIs the number of charging load curves in the jth cluster.
As a preferred technical scheme of the invention: the building steps of the power distribution system power flow analysis probability scene set in the typical day in the step S4 are as follows:
s4.1: calculating the scene number n in the power flow analysis probability scene set according to the formula (9)c-flow；
nc-flow＝nc-pv×nc-ev(9)
In formula (9), nc-flowThe number of scenes in the probability scene set of the power flow analysis is shown; n isc-pvThe clustering number is obtained when clustering is carried out on the distributed photovoltaic historical sunrise force curve; n isc-evThe clustering number is used for clustering the historical daily charging load curve of the electric vehicle charging station; s4.2: determining distributed photovoltaic output under each scene in a power flow analysis probability scene set according to a formula (10); determining charging loads of the charging stations under all scenes in the power flow analysis probability scene set according to a formula (11); calculating the probability of each scene in the power flow analysis probability scene set according to a formula (12);
Figure BDA0002453697330000051
Figure BDA0002453697330000052
pflow,k＝ppv,m×pev,nk＝1，2，···,nc-flowm＝1，2，···,nc-pvn＝1，2，···,nc-ev(12)
in the formula (10), the first and second groups,
Figure BDA0002453697330000053
the method is characterized in that the output of a distributed photovoltaic power station in a power flow analysis time period t under a power flow analysis scene k is taken from the mth scene (k is 1,2, n) in a distributed photovoltaic output probability scene setc-flow；m＝1,2,···,nc-pv；)；
In the formula (11), the reaction mixture,
Figure BDA0002453697330000054
taking the charging power of the electric vehicle charging station in the power flow analysis time period t under the power flow analysis scene k from the nth scene (n is 1,2, n) in the charging load probability scene set of the electric vehicle charging stationc-ev；)；
In the formula (12), pflow,kAnalyzing the scene probability of the scene k for the power flow;
s4.3: and calculating the equivalent active power and the equivalent reactive power of the power distribution node in a typical day under each load flow analysis scene.
As a preferred technical scheme of the invention: the method for calculating the equivalent active power of the power distribution node in the step S4.3 is that the active power output of the distributed photovoltaic power station is subtracted from the active power output of the power distribution node plus the active charging load of the electric vehicle charging station; during calculation, if the power distribution node is not connected into the electric vehicle charging station, the active charging load of the electric vehicle charging station is zero; similarly, if the power distribution node is not connected to the distributed photovoltaic power station, the active power output of the distributed photovoltaic power station is zero; the method for calculating the equivalent reactive power of the power distribution node in a typical day is that the reactive power of the power distribution node is added with the reactive power charging load of the electric vehicle charging station, and then the reactive power output of the distributed photovoltaic power station is subtracted; during calculation, if the power distribution node is not connected into the electric vehicle charging station, the reactive charging load of the electric vehicle charging station is zero; similarly, if the power distribution node is not connected to the distributed photovoltaic power station, the reactive power output of the distributed photovoltaic power station is zero; the reactive power output of the distributed photovoltaic system can be calculated according to the active power output and the power factor of the distributed photovoltaic system; the reactive charging load of the electric vehicle charging station can be calculated according to the active charging load and the power factor of the electric vehicle charging station.
Compared with the prior art, the probabilistic power flow analysis method considering the random characteristics of the charging load and the photovoltaic output has the following technical effects by adopting the technical scheme:
the method and the device can be used for performing rapid probability power flow analysis on the power distribution system after the distributed photovoltaic and electric vehicle charging station is simultaneously accessed, and provide decision reference for planners.
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FIG. 1 is a schematic diagram of the process steps of the present invention;
FIG. 2 is a schematic diagram of the method step of S2 in the present invention;
FIG. 3 is a schematic diagram of the method step of S3 in the present invention;
FIG. 4 is a schematic diagram of the method step of S4 in the present invention.
Detailed Description
The present invention will be further explained with reference to the drawings so that those skilled in the art can more deeply understand the present invention and can carry out the present invention, but the present invention will be explained below by referring to examples, which are not intended to limit the present invention.
As shown in fig. 1, the probabilistic power flow analysis method considering the random characteristics of the charging load and the photovoltaic output includes the following steps: s1: setting parameters required by the original data and the probability power flow analysis, wherein the parameters comprise: the method comprises the following steps of (1) a power distribution system topological structure, power distribution branch impedance parameters, active and reactive load curves of a power distribution node in a typical day, access positions of a distributed photovoltaic power station and an electric vehicle charging station, a historical sunrise output curve set of the distributed photovoltaic power station, a historical sunrise charging load curve set of the electric vehicle charging station, clustering numbers for clustering the historical sunrise output curve set of the distributed photovoltaic power station and the historical sunrise charging load curve set of the electric vehicle charging station, and the number of tide analysis periods in the typical day;
s2: clustering a historical sunrise output curve set of the distributed photovoltaic power station by adopting a K-means clustering method, and constructing a distributed photovoltaic output probability scene set capable of reflecting the random characteristic of the distributed photovoltaic output; the cluster number is the number of scenes in a probability scene set, each cluster center is a scene in the probability scene set, and the probability of scene occurrence is the ratio of the number of distributed photovoltaic sunrise curves in each cluster to the total number of the distributed photovoltaic sunrise curve concentration curves;
s3: clustering a historical daily charging load curve set of the electric vehicle charging station by adopting a K-means clustering method, and constructing an electric vehicle charging station charging load probability scene set capable of embodying the random characteristic of the charging load of the electric vehicle charging station; the cluster number is the number of scenes in which probability scenes are concentrated, each cluster center is a scene in which the probability scenes are concentrated, and the probability of scene occurrence is the ratio of the number of daily charging load curves in each cluster to the total number of daily load curve concentrated curves of the charging station;
s4: constructing a power distribution system power flow analysis probability scene set in a typical day on the basis of the distributed photovoltaic output probability scene set and the charging load probability scene set of the electric vehicle charging station; the method specifically comprises the following steps: according to the distributed photovoltaic output probability scene set, the charging load probability scene set of the electric vehicle charging station and the access positions of the distributed photovoltaic charging station and the electric vehicle charging station, determining the equivalent load and the scene probability of a power distribution node corresponding to each power flow analysis scene set;
s5: and carrying out power distribution system power flow calculation under all scenes in the power distribution system power flow analysis probability scene set by adopting an approximate forward-backward substitution method, summarizing power flow calculation results according to scene probabilities in the power flow analysis probability scene set, and giving power distribution system probability power flow analysis results in a typical day.
As shown in fig. 2, the step of constructing the distributed photovoltaic output probability scene set in step S2 is specifically as follows: s2.1: the number of clusters is nc-pvFrom n to nt-pvRandomly selecting n from historical solar output curves of distributed photovoltaic panelsc-pvTaking the strip curve as an initial clustering center; s2.2: calculating the distance from all the distributed photovoltaic sunrise force curves to each clustering center according to a formula (1), and classifying the distances into clusters represented by the clustering centers closest to each clustering center;
Figure BDA0002453697330000061
in the formula (1), the first and second groups,pv,iconcentrating the ith photovoltaic output curve (i ═ 1,2, ·, n) for the distributed photovoltaic historical output curvet-pv)；
Figure BDA0002453697330000071
The jth clustering center (j ═ 1,2, ·, n) of the distributed photovoltaic output curve setc-pv)；d(pv,i,
Figure BDA0002453697330000072
) The distance between the ith photovoltaic output curve and the jth clustering center is calculated; t is a power flow analysis time interval index (T is 1,2, T); t is the number of power flow analysis time periods in a typical day;pv,i,tgenerating power of the ith photovoltaic output curve in a power flow analysis time period t;
Figure BDA0002453697330000073
generating power of the jth clustering center in a power flow analysis time period t;
s2.3: calculating the mean value of all distributed photovoltaic output curves in each cluster according to a formula (2), taking the mean value as a new cluster center of each cluster, then calculating the distance from all distributed photovoltaic output curves in each cluster to the cluster center according to a formula (1), and calculating the distance mean value according to a formula (3);
Figure BDA0002453697330000074
Figure BDA0002453697330000075
in the formula (2) and the formula (3), npv,jThe number of distributed photovoltaic output curves in the jth cluster; omegajIs a set of distributed photovoltaic output curve indexes in the jth cluster; dpv-av,jThe average value of the distances from all the distributed photovoltaic output curves to the clustering center in the jth cluster;
s2.4: judging each clustering center
Figure BDA0002453697330000076
And whether the average value of the distances from all distributed photovoltaic output curves to the clustering center in the clustering is changed or not; if the change occurs, skipping to the step S2.2, and continuing clustering; otherwise, outputting a clustering result, and constructing a distributed photovoltaic output probability scene set and a clustering center
Figure BDA0002453697330000077
Namely a distributed photovoltaic output scene with concentrated probability scenes, and the probability p corresponding to each scenepv,jCan be calculated from equation (4):
Figure BDA0002453697330000078
in the formula (4), the first and second groups,npv,jis the number of distributed photovoltaic output curves in the jth cluster.
As shown in fig. 3, the step of constructing the charging load probability scene set of the electric vehicle charging station in step S3 is specifically as follows: s3.1: the number of clusters is nc-evFrom n to nt-evRandomly selecting n from historical daily charging load curve of electric vehicle charging stationc-evTaking the strip curve as an initial clustering center; s3.2: calculating the distance from the charging load curve to each clustering center on all days according to a formula (5), and classifying the distance into the cluster represented by the clustering center closest to the charging load curve;
Figure BDA0002453697330000079
in the formula (5), the first and second groups,ev,ian ith charging load curve (i ═ 1,2, ·, n) in the charging station historical daily charging load curve sett-ev)；
Figure BDA0002453697330000081
Charging the jth cluster center of the load curve set for the electric vehicle charging station (j ═ 1,2, ·, n ·c-ev)；d(ev,i,
Figure BDA0002453697330000082
) The distance between the ith charging load curve and the jth clustering center is obtained;ev,i,tcharging power of the ith charging load curve in a power flow analysis time period t;
Figure BDA0002453697330000083
charging power of the jth clustering center in a power flow analysis time period t;
s3.3: calculating the mean value of all charging load curves in each cluster according to a formula (6), making the mean value as a new cluster center of each cluster, then calculating the distance from all charging load curves in each cluster to the cluster center according to a formula (5), and calculating the distance mean value according to a formula (7);
Figure BDA0002453697330000084
Figure BDA0002453697330000085
in the formula (6) and the formula (7), nev,jIs the number of charging load curves in the jth cluster;jis the set of charging load curve indices in the jth cluster; dev-av,jThe average value of the distances from all the charging load curves to the cluster center in the jth cluster; s3.4: judging each clustering center
Figure BDA0002453697330000088
And whether the average value of the distances from all the charging load curves to the clustering center in the clustering is changed or not; if the change occurs, skipping to the step S3.2, and continuing clustering; otherwise, outputting a clustering result, constructing a charging load probability scene set and a clustering center of the electric vehicle charging station
Figure BDA0002453697330000089
That is, the charging load scenario with a concentrated probability scenario, and the probability p corresponding to each scenarioev,jCan be calculated from equation (8):
Figure BDA0002453697330000086
in the formula (8), nev,jIs the number of charging load curves in the jth cluster.
As shown in fig. 4, the power distribution system power flow analysis probability scenario set in step S4 in a typical day includes the following specific steps: s4.1: calculating the scene number n in the power flow analysis probability scene set according to the formula (9)c-flow；
nc-flow＝nc-pv×nc-ev(9)
In formula (9), nc-flowThe number of scenes in the probability scene set of the power flow analysis is shown; n isc-pvThe clustering number is obtained when clustering is carried out on the distributed photovoltaic historical sunrise force curve; n isc-evClustering is carried out on historical daily charging load curves of electric vehicle charging stationsCounting; s4.2: determining distributed photovoltaic output under each scene in a power flow analysis probability scene set according to a formula (10); determining charging loads of the charging stations under all scenes in the power flow analysis probability scene set according to a formula (11); calculating the probability of each scene in the power flow analysis probability scene set according to a formula (12);
Figure BDA0002453697330000087
Figure BDA0002453697330000091
pflow,k＝ppv,m×pev,nk＝1，2，···,nc-flowm＝1，2，···,nc-pvn＝1，2，···,nc-ev(12)
in the formula (10), the first and second groups,
Figure BDA0002453697330000092
the method is characterized in that the output of a distributed photovoltaic power station in a power flow analysis time period t under a power flow analysis scene k is taken from the mth scene (k is 1,2, n) in a distributed photovoltaic output probability scene setc-flow；m＝1,2,···,nc-pv；)；
In the formula (11), the reaction mixture,
Figure BDA0002453697330000093
taking the charging power of the electric vehicle charging station in the power flow analysis time period t under the power flow analysis scene k from the nth scene (n is 1,2, n) in the charging load probability scene set of the electric vehicle charging stationc-ev；)；
In the formula (12), pflow,kAnalyzing the scene probability of the scene k for the power flow;
s4.3: and calculating the equivalent active power and the equivalent reactive power of the power distribution node in a typical day under each load flow analysis scene.
S4.3, calculating the equivalent active power of the power distribution node in a typical day by adding the active load of the power distribution node to the active charging load of the electric vehicle charging station and then subtracting the active output of the distributed photovoltaic power station; during calculation, if the power distribution node is not connected into the electric vehicle charging station, the active charging load of the electric vehicle charging station is zero; similarly, if the power distribution node is not connected to the distributed photovoltaic power station, the active power output of the distributed photovoltaic power station is zero; the method for calculating the equivalent reactive power of the power distribution node in a typical day is that the reactive power of the power distribution node is added with the reactive power charging load of the electric vehicle charging station, and then the reactive power output of the distributed photovoltaic power station is subtracted; during calculation, if the power distribution node is not connected into the electric vehicle charging station, the reactive charging load of the electric vehicle charging station is zero; similarly, if the power distribution node is not connected to the distributed photovoltaic power station, the reactive power output of the distributed photovoltaic power station is zero; the reactive power output of the distributed photovoltaic system can be calculated according to the active power output and the power factor of the distributed photovoltaic system; the reactive charging load of the electric vehicle charging station can be calculated according to the active charging load and the power factor of the electric vehicle charging station.
Specifically, the method firstly sets the original data and parameters required by the probability power flow analysis, and comprises the following steps: the method comprises the following steps of (1) a power distribution system topological structure, power distribution branch impedance parameters, active and reactive load curves of a power distribution node in a typical day, access positions of a distributed photovoltaic power station and an electric vehicle charging station, a historical sunrise output curve set of the distributed photovoltaic power station, a historical sunrise charging load curve set of the electric vehicle charging station, clustering numbers for clustering the historical sunrise output curve set of the distributed photovoltaic power station and the historical sunrise charging load curve set of the electric vehicle charging station, and the number of tide analysis periods in the typical day; and clustering the historical solar output curve set of the distributed photovoltaic power station and the historical solar charging load curve set of the electric vehicle charging station by adopting a K-means clustering method, and respectively constructing probability scene sets capable of reflecting the random characteristics of the distributed photovoltaic output and the charging load of the electric vehicle charging station. The clustering number is the number of scenes in the probability scene set, each clustering center is the scene in the probability scene set, and the probability of scene occurrence is the ratio of the number of curve bars in each cluster to the total number of curves. And then, constructing a power distribution system power flow analysis probability scene set, namely determining the equivalent load and the scene probability of a power distribution node corresponding to each power flow analysis scene set according to the distributed photovoltaic output probability scene set, the charging load probability scene set of the electric vehicle charging station and the access positions of the distributed photovoltaic charging station and the electric vehicle charging station. And finally, performing power distribution system power flow calculation under all power flow analysis scene sets by adopting an approximate forward-backward substitution method, summarizing power flow calculation results according to scene probabilities in the power flow analysis probability scene sets, and giving power distribution system probability power flow analysis results in a typical day.
The method and the device can be used for performing rapid probability power flow analysis on the power distribution system after the distributed photovoltaic and electric vehicle charging station is simultaneously accessed, and provide decision reference for planners.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only illustrative of the present invention, and are not intended to limit the scope of the present invention, and any person skilled in the art should understand that equivalent changes and modifications made without departing from the concept and principle of the present invention should fall within the protection scope of the present invention.

Claims (5)

1. The probability power flow analysis method considering the random characteristics of the charging load and the photovoltaic output is characterized by comprising the following steps of:
s1: setting parameters required by the original data and the probability power flow analysis, wherein the parameters comprise: the method comprises the following steps of (1) a power distribution system topological structure, power distribution branch impedance parameters, active and reactive load curves of a power distribution node in a typical day, access positions of a distributed photovoltaic power station and an electric vehicle charging station, a historical sunrise output curve set of the distributed photovoltaic power station, a historical sunrise charging load curve set of the electric vehicle charging station, clustering numbers for clustering the historical sunrise output curve set of the distributed photovoltaic power station and the historical sunrise charging load curve set of the electric vehicle charging station, and the number of tide analysis periods in the typical day;
s2: clustering a historical sunrise output curve set of the distributed photovoltaic power station by adopting a K-means clustering method, and constructing a distributed photovoltaic output probability scene set capable of reflecting the random characteristic of the distributed photovoltaic output;
s3: clustering a historical daily charging load curve set of the electric vehicle charging station by adopting a K-means clustering method, and constructing an electric vehicle charging station charging load probability scene set capable of embodying the random characteristic of the charging load of the electric vehicle charging station;
s4: constructing a power distribution system power flow analysis probability scene set in a typical day on the basis of the distributed photovoltaic output probability scene set and the charging load probability scene set of the electric vehicle charging station;
s5: and carrying out power distribution system power flow calculation under all scenes in the power distribution system power flow analysis probability scene set by adopting an approximate forward-backward substitution method, summarizing power flow calculation results according to scene probabilities in the power flow analysis probability scene set, and giving power distribution system probability power flow analysis results in a typical day.
2. The method for analyzing the probability power flow considering the random characteristics of the charging load and the photovoltaic output according to claim 1, wherein the step of constructing the distributed photovoltaic output probability scene set in the step S2 is specifically as follows:
s2.1: the number of clusters is nc-pvFrom n to nt-pvRandomly selecting n from historical solar output curves of distributed photovoltaic panelsc-pvTaking the strip curve as an initial clustering center;
s2.2: calculating the distance from all the distributed photovoltaic sunrise force curves to each clustering center according to a formula (1), and classifying the distances into clusters represented by the clustering centers closest to each clustering center;
Figure FDA0002453697320000011
in the formula (1), the first and second groups,pv,iconcentrating the ith photovoltaic output curve (i ═ 1,2, ·, n) for the distributed photovoltaic historical output curvet-pv)；
Figure FDA0002453697320000012
The jth clustering center (j ═ 1,2, ·, n) of the distributed photovoltaic output curve setc-pv)；
Figure FDA0002453697320000013
The distance between the ith photovoltaic output curve and the jth clustering center is calculated; t is a power flow analysis time interval index (T is 1,2, T); t is the number of power flow analysis time periods in a typical day;pv,i,tgenerating power of the ith photovoltaic output curve in a power flow analysis time period t;
Figure FDA0002453697320000014
generating power of the jth clustering center in a power flow analysis time period t;
s2.3: calculating the mean value of all distributed photovoltaic output curves in each cluster according to a formula (2), taking the mean value as a new cluster center of each cluster, then calculating the distance from all distributed photovoltaic output curves in each cluster to the cluster center according to a formula (1), and calculating the distance mean value according to a formula (3);
Figure FDA0002453697320000021
Figure FDA0002453697320000022
in the formula (2) and the formula (3), npv,jThe number of distributed photovoltaic output curves in the jth cluster; omegajIs a set of distributed photovoltaic output curve indexes in the jth cluster; dpv-av,jThe average value of the distances from all the distributed photovoltaic output curves to the clustering center in the jth cluster;
s2.4: judging each clustering center
Figure FDA0002453697320000023
And whether the average value of the distances from all distributed photovoltaic output curves to the clustering center in the clustering is changed or not; if the change occurs, skipping to the step S2.2, and continuing clustering; otherwise, outputting a clustering result, and constructing a distributed photovoltaic output probability scene set and a clustering center
Figure FDA0002453697320000024
Namely a distributed photovoltaic output scene with concentrated probability scenes, and the probability p corresponding to each scenepv,jCan be calculated from equation (4):
Figure FDA0002453697320000025
in the formula (4), npv,jIs the number of distributed photovoltaic output curves in the jth cluster.
3. The probabilistic power flow analysis method considering the stochastic characteristics of the charging load and the photovoltaic output according to claim 1, wherein the step of constructing the probabilistic scene set of the charging load of the electric vehicle charging station in the step S3 is specifically as follows:
s3.1: the number of clusters is nc-evFrom n to nt-evRandomly selecting n from historical daily charging load curve of electric vehicle charging stationc-evTaking the strip curve as an initial clustering center;
s3.2: calculating the distance from the charging load curve to each clustering center on all days according to a formula (5), and classifying the distance into the cluster represented by the clustering center closest to the charging load curve;
Figure FDA0002453697320000026
in the formula (5), the first and second groups,ev,ian ith charging load curve (i ═ 1,2, ·, n) in the charging station historical daily charging load curve sett-ev)；
Figure FDA0002453697320000027
Charging the jth cluster center of the load curve set for the electric vehicle charging station (j ═ 1,2, ·, n ·c-ev)；
Figure FDA0002453697320000028
The distance between the ith charging load curve and the jth clustering center is obtained;ev,i,tcharging the ith stripCharging power of the load curve in a power flow analysis time period t;
Figure FDA0002453697320000029
charging power of the jth clustering center in a power flow analysis time period t;
s3.3: calculating the mean value of all charging load curves in each cluster according to a formula (6), making the mean value as a new cluster center of each cluster, then calculating the distance from all charging load curves in each cluster to the cluster center according to a formula (5), and calculating the distance mean value according to a formula (7);
Figure FDA0002453697320000031
Figure FDA0002453697320000032
in the formula (6) and the formula (7), nev,jIs the number of charging load curves in the jth cluster;jis the set of charging load curve indices in the jth cluster; dev-av,jThe average value of the distances from all the charging load curves to the cluster center in the jth cluster; s3.4: judging each clustering center
Figure FDA0002453697320000033
And whether the average value of the distances from all the charging load curves to the clustering center in the clustering is changed or not; if the change occurs, skipping to the step S3.2, and continuing clustering; otherwise, outputting a clustering result, constructing a charging load probability scene set and a clustering center of the electric vehicle charging station
Figure FDA0002453697320000034
That is, the charging load scenario with a concentrated probability scenario, and the probability p corresponding to each scenarioev,jCan be calculated from equation (8):
Figure FDA0002453697320000035
in the formula (8), nev,jIs the number of charging load curves in the jth cluster.
4. The method for analyzing the probability power flow considering the stochastic characteristics of the charging load and the photovoltaic output according to claim 1, wherein the step of constructing the power distribution system power flow analysis probability scenario set in the step S4 within a typical day is as follows:
s4.1: calculating the scene number n in the power flow analysis probability scene set according to the formula (9)c-flow；
nc-flow＝nc-pv×nc-ev(9)
In formula (9), nc-flowThe number of scenes in the probability scene set of the power flow analysis is shown; n isc-pvThe clustering number is obtained when clustering is carried out on the distributed photovoltaic historical sunrise force curve; n isc-evThe clustering number is used for clustering the historical daily charging load curve of the electric vehicle charging station; s4.2: determining distributed photovoltaic output under each scene in a power flow analysis probability scene set according to a formula (10); determining charging loads of the charging stations under all scenes in the power flow analysis probability scene set according to a formula (11); calculating the probability of each scene in the power flow analysis probability scene set according to a formula (12);
Figure FDA0002453697320000036
Figure FDA0002453697320000037
pflow,k＝ppv,m×pev,nk＝1，2，···,nc-flowm＝1，2，···,nc-pvn＝1，2，···,nc-ev(12)
in the formula (10), the first and second groups,
Figure FDA0002453697320000038
the output of the distributed photovoltaic power station in the power flow analysis time period t under the power flow analysis scene k is obtained from the distributed photovoltaic outputThe mth scene (k ═ 1,2, ·, n) in the set of probabilistic scenesc-flow；m＝1,2,···,nc-pv；)；
In the formula (11), the reaction mixture,
Figure FDA0002453697320000041
taking the charging power of the electric vehicle charging station in the power flow analysis time period t under the power flow analysis scene k from the nth scene (n is 1,2, n) in the charging load probability scene set of the electric vehicle charging stationc-ev；)；
In the formula (12), pflow,kAnalyzing the scene probability of the scene k for the power flow;
s4.3: and calculating the equivalent active power and the equivalent reactive power of the power distribution node in a typical day under each load flow analysis scene.
5. The method for analyzing probability power flow considering stochastic characteristics of charging load and photovoltaic output according to claim 4, wherein the method for calculating the equivalent active power of the distribution node in the step S4.3 is that the active load of the distribution node is added to the active charging load of the electric vehicle charging station and then subtracted from the active output of the distributed photovoltaic power station; during calculation, if the power distribution node is not connected into the electric vehicle charging station, the active charging load of the electric vehicle charging station is zero; similarly, if the power distribution node is not connected to the distributed photovoltaic power station, the active power output of the distributed photovoltaic power station is zero; the method for calculating the equivalent reactive power of the power distribution node in a typical day is that the reactive power of the power distribution node is added with the reactive power charging load of the electric vehicle charging station, and then the reactive power output of the distributed photovoltaic power station is subtracted; during calculation, if the power distribution node is not connected into the electric vehicle charging station, the reactive charging load of the electric vehicle charging station is zero; similarly, if the power distribution node is not connected to the distributed photovoltaic power station, the reactive power output of the distributed photovoltaic power station is zero; the reactive power output of the distributed photovoltaic system can be calculated according to the active power output and the power factor of the distributed photovoltaic system; the reactive charging load of the electric vehicle charging station can be calculated according to the active charging load and the power factor of the electric vehicle charging station.
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